Bioc2021: Visualisation of highly-multiplexed imaging data in R
23 July 2021
Abstract
Highly multiplexed imaging cytometry acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualized across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualized on segmented cell areas. This package contains functions for the visualization of multiplexed read-outs and cell-level information obtained by multiplexed imaging cytometry. The main functions of this package allow 1. the visualization of pixel-level information across multiple channels and 2. the display of cell-level information (expression and/or metadata) on segmentation masks.
Package
cytomapper 1.4.1
1 Data and code availability
To follow this tutorial, please visit https://github.com/BodenmillerGroup/cytomapper_demos/docs. The compiled .html of this workshop is hosted at: https://bodenmillergroup.github.io/cytomapper_demos. The cytomapper package can be installed via:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("cytomapper")To reproduce the analysis, clone the repository:
git clone https://github.com/BodenmillerGroup/cytomapper_demos.gitand open the Bioc2021_workshop.Rmd file in the docs folder.
We provide three images, their segmentation masks and the quantified single-cell
data in form of a SingleCellExperiment object in the data folder. The data
is taken from A map of human type 1 diabetes progression by imaging mass
cytometry.
2 Introduction
The analysis and visualization of highly-multiplexed imaging data relies on three types of data structures:
Multi-channel images realized as three dimensional arrays where the numeric entry of each voxel represents the intensity of the pixel at position
xandyfor channelc.Single-channel segmentation masks realized as matrices where sets of pixels with the same integer ID represent individual objects (here, these are segmented cells)
A table containing the quantified features per cell and channel (e.g. mean pixel intensity)
The cytomapper package handles these data types using objects of the following
S4 classes:
Multiple multi-channel images are stored in form of
EBImage::Imageobjects within acytomapper::CytoImageListobjectMultiple single-channel segmentation masks are stored in form of
EBImage::Imageobjects within acytomapper::CytoImageListobjectPer-cell intensity measures and cell-/channel-specific metadata are stored in a
SingleCellExperiment::SingleCellExperimentcontainer
cytomapper overview figure. A) The plotCells function combines a SingleCellExperiment and CytoImageList object to visualize marker expression or cell-specific metadata on segmentation masks. B) The plotPixels function requires a CytoImageList object to visualize the combined expression of up to six markers as composite images
The cytomapper package contains three broad functionalities:
Visualization of pixel-intensities as composites of up to six channels (
plotPixels)Visualization of cell-specific features on segmentation masks (
plotCells)Interactive gating of cells and visualization of gated cells on images (
cytomapperShiny)
The follwing demonstration gives an overview on (1) data handling and processing, (2) data visualization and (3) interactive analysis.
We use imaging mass cytometry data
to highlight the functionality of the cytomapper package. However, any imaging
technology is supported as long as the data can be read into R (memory
restrictions and file type restrictions.)
The raw data has been processed using the ImcSegmentationPipeline. In this tutorial, we assume that image segmentation has been performed beforehand.
3 Reading in the data
The following section describes how to read in images (e.g. in .tiff format)
into CytoImageList objects and how to generate SingleCellExperiment
objects from these images.
3.1 Reading in images
The cytomapper::loadImages function reads in multi-channel images and segmentation masks into CytoImageList objects.
library(cytomapper)
# Read in 32-bit multi-channel images
(images <- loadImages("../data/images/", pattern = ".tiff"))## CytoImageList containing 3 image(s)
## names(3): E30_a0_full_clean G23_a0_full_clean J01_a0_full_clean
## Each image contains 38 channel(s)# Read in 16-bit unsigned integer segmentation masks
(masks <- loadImages("../data/masks/", pattern = ".tiff"))## CytoImageList containing 3 image(s)
## names(3): E30_a0_full_mask G23_a0_full_mask J01_a0_full_mask
## Each image contains 1 channelIt is always recommended to observe the numeric pixel values to make sure that images were read in correctly:
# multi-channel images - first image, first channel
hist(log10(images[[1]][,,1] + 1))
# Segmentation mask
masks[[1]]## Image
## colorMode : Grayscale
## storage.mode : double
## dim : 390 452
## frames.total : 1
## frames.render: 1
##
## imageData(object)[1:5,1:6]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.577707e-05
## [2,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.577707e-05
## [3,] 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05
## [4,] 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05
## [5,] 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05 4.577707e-05
## [,6]
## [1,] 0.000000e+00
## [2,] 0.000000e+00
## [3,] 4.577707e-05
## [4,] 4.577707e-05
## [5,] 4.577707e-05We notice, that the segmentation masks were not read in as integer images.
This behavior arises due to issues with accessing the tiff metadata after
pre-processing using CellProfiler.
In these cases, the cytomapper::scaleImages function can be used to rescale
segmentation masks to only contain interger IDs. Here, the scaling factor
is 2 ^ 16 - 1 = 65535 accounting for the 16-bit unsigned integer encoding.
masks <- scaleImages(masks, 2 ^ 16 - 1)
masks[[1]]## Image
## colorMode : Grayscale
## storage.mode : double
## dim : 390 452
## frames.total : 1
## frames.render: 1
##
## imageData(object)[1:5,1:6]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 0 3 0
## [2,] 0 0 0 0 3 0
## [3,] 3 3 3 3 3 3
## [4,] 3 3 3 3 3 3
## [5,] 3 3 3 3 3 3As an alternative, while reading in the images, the as.is option can be set to TRUE.
masks <- loadImages("../data/masks/", pattern = ".tiff", as.is = TRUE)
masks[[1]]## Image
## colorMode : Grayscale
## storage.mode : integer
## dim : 390 452
## frames.total : 1
## frames.render: 1
##
## imageData(object)[1:5,1:6]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 0 3 0
## [2,] 0 0 0 0 3 0
## [3,] 3 3 3 3 3 3
## [4,] 3 3 3 3 3 3
## [5,] 3 3 3 3 3 3We can already visualize the segmentation mask to get an idea of the tissue structure:
plotCells(masks)
3.1.1 Reading data to disk
To increase scalability of the cytomapper package, images can be stored on
disk making use of the HDF5Array and DelayedArray package. Reading in the
data can also be parallelised, which is only recommended for large images.
format(object.size(images), units = "Kb")## [1] "170235.4 Kb"images_ondisk <- loadImages("../data/images/", pattern = ".tiff",
on_disk = TRUE, h5FilesPath = "../data/images",
BPPARAM = BiocParallel::bpparam())
format(object.size(images_ondisk), units = "Kb")## [1] "10.1 Kb"All cytomapper functions support images and masks stored on disk.
3.2 The SingleCellExperiment object
In the original analysis, single-cell features have been extracted using
CellProfiler. For this tutorial, we provide a SingleCellExperiment object,
which already contains the mean intensities per cell and channel and all
relevant metadata (e.g. cell-type annotation).
The SingleCellExperiment can be read-in form the data folder:
(sce <- readRDS("../data/sce.rds"))## class: SingleCellExperiment
## dim: 38 5819
## metadata(0):
## assays(2): counts exprs
## rownames(38): H3 SMA ... Ir191 Ir193
## rowData names(15): TubeNb MetalTag ... miCAT2 miCAT
## colnames(5819): E30_1 E30_2 ... J01_2173 J01_2174
## colData names(26): slide id ... Ethnicity BMI
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):colData(sce)## DataFrame with 5819 rows and 26 columns
## slide id ImageNumber CellNumber Pos_X Pos_Y
## <character> <character> <integer> <integer> <numeric> <numeric>
## E30_1 E E30_1 166 1 171.375 0.62500
## E30_2 E E30_2 166 2 149.400 1.60000
## E30_3 E E30_3 166 3 6.400 3.36471
## E30_4 E E30_4 166 4 220.756 2.85366
## E30_5 E E30_5 166 5 189.523 4.95455
## ... ... ... ... ... ... ...
## J01_2170 J J01_2170 286 2170 435.950 453.050
## J01_2171 J J01_2171 286 2171 456.469 452.688
## J01_2172 J J01_2172 286 2172 340.643 453.643
## J01_2173 J J01_2173 286 2173 19.900 454.500
## J01_2174 J J01_2174 286 2174 43.250 454.625
## ParentIslet ClosestIslet Area NbNeighbours ImageFullName
## <integer> <integer> <integer> <integer> <character>
## E30_1 0 1 8 2 E30_a0_full_clean.tiff
## E30_2 0 1 5 2 E30_a0_full_clean.tiff
## E30_3 0 1 85 3 E30_a0_full_clean.tiff
## E30_4 0 1 41 4 E30_a0_full_clean.tiff
## E30_5 0 1 44 5 E30_a0_full_clean.tiff
## ... ... ... ... ... ...
## J01_2170 0 1 40 4 J01_a0_full_clean.tiff
## J01_2171 0 1 64 3 J01_a0_full_clean.tiff
## J01_2172 0 1 42 3 J01_a0_full_clean.tiff
## J01_2173 0 1 10 3 J01_a0_full_clean.tiff
## J01_2174 0 1 16 2 J01_a0_full_clean.tiff
## width height ImageName CellCat CellType case
## <integer> <integer> <character> <character> <character> <integer>
## E30_1 390 452 E30 exocrine ductal 6126
## E30_2 390 452 E30 immune macrophage 6126
## E30_3 390 452 E30 exocrine acinar 6126
## E30_4 390 452 E30 exocrine acinar 6126
## E30_5 390 452 E30 exocrine ductal 6126
## ... ... ... ... ... ... ...
## J01_2170 463 456 J01 exocrine ductal 6180
## J01_2171 463 456 J01 exocrine acinar 6180
## J01_2172 463 456 J01 exocrine acinar 6180
## J01_2173 463 456 J01 exocrine acinar 6180
## J01_2174 463 456 J01 exocrine acinar 6180
## part group stage duration Aab.status Age
## <character> <integer> <character> <numeric> <character> <numeric>
## E30_1 Tail 1 Non-diabetic NA Negative 25.2
## E30_2 Tail 1 Non-diabetic NA Negative 25.2
## E30_3 Tail 1 Non-diabetic NA Negative 25.2
## E30_4 Tail 1 Non-diabetic NA Negative 25.2
## E30_5 Tail 1 Non-diabetic NA Negative 25.2
## ... ... ... ... ... ... ...
## J01_2170 Tail 2 Long-duration 11 Positive 27
## J01_2171 Tail 2 Long-duration 11 Positive 27
## J01_2172 Tail 2 Long-duration 11 Positive 27
## J01_2173 Tail 2 Long-duration 11 Positive 27
## J01_2174 Tail 2 Long-duration 11 Positive 27
## Gender Ethnicity BMI
## <character> <character> <numeric>
## E30_1 M Caucasian 25.1
## E30_2 M Caucasian 25.1
## E30_3 M Caucasian 25.1
## E30_4 M Caucasian 25.1
## E30_5 M Caucasian 25.1
## ... ... ... ...
## J01_2170 M Caucasian 25.9
## J01_2171 M Caucasian 25.9
## J01_2172 M Caucasian 25.9
## J01_2173 M Caucasian 25.9
## J01_2174 M Caucasian 25.94 Image pre-processing
The following section will discuss setting image metadata and image normalization.
4.1 Setting metadata
Before image visualization, there are a few metadata that need to be set.
We will need to set the channel names of the images via the channelNames
getter/setter function. The channel order here is the same as the row order of
the SingleCellExperiment object. We will also need to synchronise the image
IDs across the multi-channel images and segmentation masks by storing a
DataFrame in the elementMetadata slot of the CytoImageList object.
# Add channel names
channelNames(images) <- rownames(sce)
# Add image name to metadata
(mcols(images) <- mcols(masks) <- DataFrame(ImageName = c("E30", "G23", "J01")))## DataFrame with 3 rows and 1 column
## ImageName
## <character>
## 1 E30
## 2 G23
## 3 J014.2 Normalization
Channel normalization is a crucial step for image visualization to enhance
the visibility of biological signals. One option is to transform (e.g. sqrt)
transform the images. A more widely used alternative is to perform a range-scaling
and clipping of channel intensities.
The cytomapper package exports the normalize function that scales channels
between 0 and 1 across all images (by default). Clipping is performed by setting
the inputRange parameter in the normalize function.
# Min-max normalization
images_norm <- normalize(images)
# Clip the images
images_norm <- normalize(images_norm, inputRange = c(0, 0.2))4.3 Measuring object features
The cytomapper::measureObjects function takes the segmentation masks and
multi- channel image objects and computes morphological (e.g. cell shape, size
and location) and intensity features (default: mean intensity per channel and
object/cell).
sce_measured <- measureObjects(mask = masks, image = images, img_id = "ImageName")
sce_measured## class: SingleCellExperiment
## dim: 38 5819
## metadata(0):
## assays(1): counts
## rownames(38): H3 SMA ... Ir191 Ir193
## rowData names(0):
## colnames: NULL
## colData names(8): ImageName object_id ... m.majoraxis m.eccentricity
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):The pixel intensities per cell can be summarized in different ways (e.g. as
quantiles). Furthermore, parallelization is possible by setting BPPARAM = bpparam().
Cell-specific morphological features are stored in colData(sce) while the
mean pixel intensities per cell and channel are stored in counts(sce).
colData(sce_measured)## DataFrame with 5819 rows and 8 columns
## ImageName object_id s.area s.radius.mean m.cx m.cy
## <character> <numeric> <numeric> <numeric> <numeric> <numeric>
## 1 E30 1 8 1.132373 172.375 1.62500
## 2 E30 2 5 0.969029 150.400 2.60000
## 3 E30 3 85 4.928794 7.400 4.36471
## 4 E30 4 41 3.148247 221.756 3.85366
## 5 E30 5 44 3.454595 190.523 5.95455
## ... ... ... ... ... ... ...
## 5815 J01 2170 40 3.15461 436.950 454.050
## 5816 J01 2171 64 4.26023 457.469 453.688
## 5817 J01 2172 42 3.51294 341.643 454.643
## 5818 J01 2173 10 1.55575 20.900 455.500
## 5819 J01 2174 16 2.46488 44.250 455.625
## m.majoraxis m.eccentricity
## <numeric> <numeric>
## 1 3.96961 0.713215
## 2 4.34170 0.955796
## 3 13.40271 0.767751
## 4 8.17150 0.515845
## 5 10.23176 0.792695
## ... ... ...
## 5815 8.85424 0.722069
## 5816 12.40952 0.812136
## 5817 12.96285 0.936558
## 5818 6.43099 0.928765
## 5819 10.74725 0.985845counts(sce_measured)[1:10, 1:10]## [,1] [,2] [,3] [,4] [,5] [,6]
## H3 22.6329811 2.4361224 13.08346010 6.7423978 14.72265212 11.5127982
## SMA 0.7880026 1.0405174 1.26743278 0.9876558 0.32023911 0.7070429
## INS 0.0000000 0.0000000 0.24131852 0.2329416 0.16629238 0.2158641
## CD38 0.3090078 0.2992626 0.32912218 0.1752310 0.20383297 0.2546964
## CD44 2.3301406 0.4281695 0.59169109 0.4615597 1.78500872 0.4622892
## PCSK2 1.1045258 1.0593891 0.56905349 0.5634079 0.85554019 0.7053719
## CD99 0.3115875 0.3936061 0.77623081 1.0573374 0.89659225 0.9886102
## CD68 0.0000000 4.7920089 0.31981109 0.4370047 1.25933963 0.2232831
## MPO 0.0000000 5.1032834 0.07681261 0.1129901 0.04637183 0.1006678
## SLC2A1 2.8200562 2.2636643 0.79776477 1.4814364 1.74246915 0.4607339
## [,7] [,8] [,9] [,10]
## H3 6.65392760 8.7451991 6.6297040 9.66234309
## SMA 1.94122101 1.2825997 1.3894975 0.78360098
## INS 0.09161043 0.1462787 0.1709020 0.09961374
## CD38 0.26884190 0.5762133 0.3820182 0.50709601
## CD44 0.40098510 0.5384129 0.3380702 0.75498592
## PCSK2 0.65091868 0.8107124 1.0038303 0.68700536
## CD99 0.73302101 0.7973502 0.5520242 0.94204760
## CD68 1.04103449 0.7391340 0.2099665 1.36866162
## MPO 3.35050052 5.6631805 0.6724382 8.20452772
## SLC2A1 1.61920737 2.3110813 1.0785549 2.689120755 Data visualization
After having formatted the data, we can now move on to data visualization.
5.1 Visualizing pixel intensities
The cytomapper::plotPixels function is the main function for visualization
of pixel intensities.
The easiest way of visualizing images is to just specify channel names:
plotPixels(images,
colour_by = c("PIN", "CDH"))
Here, the intensities appear weak. For this, we have normalized the images:
plotPixels(images_norm,
colour_by = c("PIN", "CDH"))
We can see a decline in pro-insulin expression while E-cadherin expression stays the same.
Instead of normalizing the images, you can also specify the background (b), contrast (c) and gamma (g) level of the images:
plotPixels(images,
colour_by = c("PIN", "CDH"),
bcg = list(PIN = c(0, 5, 1),
CDH = c(0, 5, 1)))
The cytomapper package allows flexible adjustments of all visual attributes of the images:
plotPixels(
image = images,
colour_by = c("PIN", "CD4", "CD8a"),
colour = list(PIN = c("black", "yellow"),
CD4 = c("black", "blue"),
CD8a = c("black", "red")),
bcg = list(PIN = c(0, 10, 1),
CD4 = c(0, 8, 1),
CD8a = c(0, 10, 1)),
image_title = list(
text = c("Non-diabetic",
"Recent onset T1D",
"Long duration T1D")
),
scale_bar = list(
length = 100,
label = expression("100 " ~ mu * "m")
))
The colour_by parameter defines the channel names by which to colour the
composite. Per channel, a colour scale is generated by setting colour. The
attributes of the image titles can be set via the parameter image_title and
attributes of the scale bar are set via the parameter scale_bar.
To see all available parameter options, refer to ?plotting-param
5.2 Visualizing cells
In the next step, we can visualize mean pixel intensities and cell-specific metadata directly on the segmentation masks.
For this, we will use the cytomapper::plotCells function while combining the
SingleCellExperiment object and the CytoImageList storing the segmentation
masks:
plotCells(mask = masks, object = sce,
cell_id = "CellNumber", img_id = "ImageName",
colour_by = c("PIN", "CD8a", "CD4"),
exprs_values = "exprs")
The assay(sce, "exprs") slot stores the asinh-transformed mean pixel intensities
per channel and cell. The cell_id entry needs to be stored in the colData(sce)
slot to link each individual cell to their corresponding pixels in the segmentation mask.
The img_id parameter specifies the colData(sce) entry storing the name/ID
of the image to which each cell belongs. This should also be an entry in
mcols(masks).
The SingleCellExperiment object is subsettable to highlight only certain cells:
cur_sce <- sce[,sce$CellType %in%
c("beta", "alpha", "delta", "Tc", "Th")]
plotCells(mask = masks, object = cur_sce,
cell_id = "CellNumber", img_id = "ImageName",
colour_by = c("PIN", "CD8a", "CD4"),
exprs_values = "exprs",
missing_colour = "white")
Here, missing_colour defines the colour of cells missing in the SingleCellExperiment object.
This can also be useful when visualizing cell-specific metadata:
plotCells(
mask = masks,
object = cur_sce,
cell_id = "CellNumber",
img_id = "ImageName",
colour_by = "CellType",
image_title = list(
text = c("Non-diabetic",
"Recent onset T1D",
"Long duration T1D"),
colour = "black"),
scale_bar = list(
length = 100,
label = expression("100 " ~ mu * "m"),
colour = "black"),
missing_colour = "white",
background_colour = "gray")
5.3 Visualizing cells and pixel intensities
Finally, all three objects (multi-channel images, segmentation masks and the single-cell data) can be combined to outline cells on composite images.
As a first demonstration, we select only pancreatic beta cells and visualize their corresponing marker pro-insulin:
cur_sce <- sce[,sce$CellType == "beta"]
plotPixels(images_norm,
mask = masks,
object = cur_sce,
cell_id = "CellNumber",
img_id = "ImageName",
colour_by = c("PIN", "H3"),
bcg = list(H3 = c(0, 2, 1)),
colour = list(PIN = c("black", "yellow"),
H3 = c("black", "blue"),
CellType = c(beta = "red")),
outline_by = "CellType")
One can now also visualize multiple cell types and multiple markers together:
6 Interactive visualization
7 Further resources
imcdatasets
imcRtools
steinbock
imcsegmentationpipeline
Session info
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] cytomapper_1.4.1 SingleCellExperiment_1.14.1
## [3] SummarizedExperiment_1.22.0 Biobase_2.52.0
## [5] GenomicRanges_1.44.0 GenomeInfoDb_1.28.1
## [7] IRanges_2.26.0 S4Vectors_0.30.0
## [9] BiocGenerics_0.38.0 MatrixGenerics_1.4.0
## [11] matrixStats_0.59.0 EBImage_4.34.0
## [13] BiocStyle_2.20.2
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 RColorBrewer_1.1-2 tools_4.1.0
## [4] bslib_0.2.5.1 utf8_1.2.1 R6_2.5.0
## [7] svgPanZoom_0.3.4 HDF5Array_1.20.0 vipor_0.4.5
## [10] DBI_1.1.1 colorspace_2.0-2 rhdf5filters_1.4.0
## [13] raster_3.4-13 sp_1.4-5 gridExtra_2.3
## [16] tidyselect_1.1.1 compiler_4.1.0 DelayedArray_0.18.0
## [19] bookdown_0.22 sass_0.4.0 scales_1.1.1
## [22] systemfonts_1.0.2 stringr_1.4.0 digest_0.6.27
## [25] tiff_0.1-8 fftwtools_0.9-11 rmarkdown_2.9
## [28] svglite_2.0.0 XVector_0.32.0 jpeg_0.1-8.1
## [31] pkgconfig_2.0.3 htmltools_0.5.1.1 highr_0.9
## [34] fastmap_1.1.0 htmlwidgets_1.5.3 rlang_0.4.11
## [37] shiny_1.6.0 jquerylib_0.1.4 generics_0.1.0
## [40] jsonlite_1.7.2 BiocParallel_1.26.1 dplyr_1.0.7
## [43] RCurl_1.98-1.3 magrittr_2.0.1 GenomeInfoDbData_1.2.6
## [46] Matrix_1.3-3 Rcpp_1.0.7 ggbeeswarm_0.6.0
## [49] munsell_0.5.0 Rhdf5lib_1.14.2 fansi_0.5.0
## [52] viridis_0.6.1 abind_1.4-5 lifecycle_1.0.0
## [55] stringi_1.7.3 yaml_2.2.1 zlibbioc_1.38.0
## [58] rhdf5_2.36.0 grid_4.1.0 promises_1.2.0.1
## [61] shinydashboard_0.7.1 crayon_1.4.1 lattice_0.20-44
## [64] magick_2.7.2 locfit_1.5-9.4 knitr_1.33
## [67] pillar_1.6.1 codetools_0.2-18 glue_1.4.2
## [70] evaluate_0.14 BiocManager_1.30.16 png_0.1-7
## [73] vctrs_0.3.8 httpuv_1.6.1 gtable_0.3.0
## [76] purrr_0.3.4 assertthat_0.2.1 ggplot2_3.3.5
## [79] xfun_0.24 mime_0.11 xtable_1.8-4
## [82] later_1.2.0 viridisLite_0.4.0 tibble_3.1.2
## [85] beeswarm_0.4.0 ellipsis_0.3.2